using Gadfly, RDatasets
set_default_plot_size(21cm, 8cm)
p1 = plot(dataset("datasets", "iris"), x="SepalLength", y="SepalWidth",
Geom.point)
p2 = plot(dataset("datasets", "iris"), x="SepalLength", y="SepalWidth",
Stat.binmean, Geom.point)
hstack(p1,p2)
SepalLength
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
0
5
10
15
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
8.2
8.4
8.6
8.8
9.0
9.2
9.4
9.6
9.8
10.0
10.2
10.4
10.6
10.8
11.0
11.2
11.4
11.6
11.8
12.0
7.6253.0875
7.1400000000000013.2
6.8571428571428563.0714285714285716
6.68000000000000153.0300000000000002
6.5,3.0
6.39999999999999952.9571428571428577
6.2999999999999992.8555555555555556
6.22.8249999999999997
6.052.7916666666666665
5.9000000000000013.0666666666666664
5.7999999999999992.8857142857142857
5.7000000000000013.1
5.60000000000000052.816666666666667
5.4428571428571433.207142857142857
5.2,3.425
5.10000000000000053.477777777777778
4.96253.05625
4.7714285714285713.185714285714286
4.4888888888888893.0777777777777775
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
1.75
2.00
2.25
2.50
2.75
3.00
3.25
3.50
3.75
4.00
4.25
4.50
2.00
2.05
2.10
2.15
2.20
2.25
2.30
2.35
2.40
2.45
2.50
2.55
2.60
2.65
2.70
2.75
2.80
2.85
2.90
2.95
3.00
3.05
3.10
3.15
3.20
3.25
3.30
3.35
3.40
3.45
3.50
3.55
3.60
3.65
3.70
3.75
3.80
3.85
3.90
3.95
4.00
4.05
4.10
4.15
4.20
4.25
2
3
4
5
2.00
2.05
2.10
2.15
2.20
2.25
2.30
2.35
2.40
2.45
2.50
2.55
2.60
2.65
2.70
2.75
2.80
2.85
2.90
2.95
3.00
3.05
3.10
3.15
3.20
3.25
3.30
3.35
3.40
3.45
3.50
3.55
3.60
3.65
3.70
3.75
3.80
3.85
3.90
3.95
4.00
4.05
4.10
4.15
4.20
4.25
SepalWidth
SepalLength
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
0
5
10
15
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
8.2
8.4
8.6
8.8
9.0
9.2
9.4
9.6
9.8
10.0
10.2
10.4
10.6
10.8
11.0
11.2
11.4
11.6
11.8
12.0
5.9,3.0
6.2,3.4
6.5,3.0
6.3,2.5
6.7,3.0
6.7,3.3
6.8,3.2
5.8,2.7
6.9,3.1
6.7,3.1
6.9,3.1
6.0,3.0
6.4,3.1
6.3,3.4
7.7,3.0
6.1,2.6
6.3,2.8
6.4,2.8
7.9,3.8
7.4,2.8
7.2,3.0
6.4,2.8
6.1,3.0
6.2,2.8
7.2,3.2
6.7,3.3
6.3,2.7
7.7,2.8
5.6,2.8
6.9,3.2
6.0,2.2
7.7,2.6
7.7,3.8
6.5,3.0
6.4,3.2
5.8,2.8
5.7,2.5
6.8,3.0
6.4,2.7
6.5,3.2
7.2,3.6
6.7,2.5
7.3,2.9
4.9,2.5
7.6,3.0
6.5,3.0
6.3,2.9
7.1,3.0
5.8,2.7
6.3,3.3
5.7,2.8
5.1,2.5
6.2,2.9
5.7,2.9
5.7,3.0
5.6,2.7
5.0,2.3
5.8,2.6
6.1,3.0
5.5,2.6
5.5,2.5
5.6,3.0
6.3,2.3
6.7,3.1
6.0,3.4
5.4,3.0
6.0,2.7
5.8,2.7
5.5,2.4
5.5,2.4
5.7,2.6
6.0,2.9
6.7,3.0
6.8,2.8
6.6,3.0
6.4,2.9
6.1,2.8
6.3,2.5
6.1,2.8
5.9,3.2
5.6,2.5
6.2,2.2
5.8,2.7
5.6,3.0
6.7,3.1
5.6,2.9
6.1,2.9
6.0,2.2
5.9,3.0
5.0,2.0
5.2,2.7
6.6,2.9
4.9,2.4
6.3,3.3
5.7,2.8
6.5,2.8
5.5,2.3
6.9,3.1
6.4,3.2
7.0,3.2
5.0,3.3
5.3,3.7
4.6,3.2
5.1,3.8
4.8,3.0
5.1,3.8
5.0,3.5
4.4,3.2
4.5,2.3
5.0,3.5
5.1,3.4
4.4,3.0
4.9,3.6
5.5,3.5
5.0,3.2
4.9,3.1
5.5,4.2
5.2,4.1
5.4,3.4
4.8,3.1
4.7,3.2
5.2,3.4
5.2,3.5
5.0,3.4
5.0,3.0
4.8,3.4
5.1,3.3
4.6,3.6
5.1,3.7
5.4,3.4
5.1,3.8
5.7,3.8
5.1,3.5
5.4,3.9
5.7,4.4
5.8,4.0
4.3,3.0
4.8,3.0
4.8,3.4
5.4,3.7
4.9,3.1
4.4,2.9
5.0,3.4
4.6,3.4
5.4,3.9
5.0,3.6
4.6,3.1
4.7,3.2
4.9,3.0
5.1,3.5
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
-2.5
0.0
2.5
5.0
7.5
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
3.0
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
4.0
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
5.0
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
6.0
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
6.9
7.0
SepalWidth
using DataFrames, Gadfly, Distributions
set_default_plot_size(21cm, 8cm)
x = -4:0.1:4
Da = [DataFrame(x=x, ymax=pdf.(Normal(μ),x), ymin=0.0, u="μ=$μ") for μ in [-1,1]]
Db = [DataFrame(x=randn(200).+μ, u="μ=$μ") for μ in [-1,1]]
p1 = plot(vcat(Da...), x=:x, y=:ymax, ymin=:ymin, ymax=:ymax, color=:u,
Geom.line, Geom.ribbon, Guide.ylabel("Density"), Theme(alphas=[0.6]),
Guide.colorkey(title="", pos=[2.5,0.6]), Guide.title("Parametric PDF")
)
p2 = plot(vcat(Db...), x=:x, color=:u, Theme(alphas=[0.6]),
Stat.density(bandwidth=0.5), Geom.polygon(fill=true, preserve_order=true),
Coord.cartesian(xmin=-4, xmax=4, ymin=0, ymax=0.4),
Guide.colorkey(title="", pos=[2.5,0.6]), Guide.title("Kernel PDF")
)
hstack(p1,p2)
x
-14
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
14
-12.0
-11.5
-11.0
-10.5
-10.0
-9.5
-9.0
-8.5
-8.0
-7.5
-7.0
-6.5
-6.0
-5.5
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
-20
-10
0
10
20
-12.0
-11.5
-11.0
-10.5
-10.0
-9.5
-9.0
-8.5
-8.0
-7.5
-7.0
-6.5
-6.0
-5.5
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
μ=-1
μ=1
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
-0.5
0.0
0.5
1.0
-0.40
-0.38
-0.36
-0.34
-0.32
-0.30
-0.28
-0.26
-0.24
-0.22
-0.20
-0.18
-0.16
-0.14
-0.12
-0.10
-0.08
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
0.26
0.28
0.30
0.32
0.34
0.36
0.38
0.40
0.42
0.44
0.46
0.48
0.50
0.52
0.54
0.56
0.58
0.60
0.62
0.64
0.66
0.68
0.70
0.72
0.74
0.76
0.78
0.80
0.82
Kernel PDF
x
-14
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
14
-12.0
-11.5
-11.0
-10.5
-10.0
-9.5
-9.0
-8.5
-8.0
-7.5
-7.0
-6.5
-6.0
-5.5
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
-20
-10
0
10
20
-12.0
-11.5
-11.0
-10.5
-10.0
-9.5
-9.0
-8.5
-8.0
-7.5
-7.0
-6.5
-6.0
-5.5
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
μ=-1
μ=1
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
-0.5
0.0
0.5
1.0
-0.40
-0.38
-0.36
-0.34
-0.32
-0.30
-0.28
-0.26
-0.24
-0.22
-0.20
-0.18
-0.16
-0.14
-0.12
-0.10
-0.08
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
0.26
0.28
0.30
0.32
0.34
0.36
0.38
0.40
0.42
0.44
0.46
0.48
0.50
0.52
0.54
0.56
0.58
0.60
0.62
0.64
0.66
0.68
0.70
0.72
0.74
0.76
0.78
0.80
0.82
Density
Parametric PDF
using CategoricalArrays
using Gadfly
set_default_plot_size(14cm, 8cm)
n = 400
group = repeat([-1, 1], inner=200)
x = randn(n) .+ group
plot(x=x, color=categorical(group), Guide.colorkey(title="", pos=[3.6,0.7]),
layer(Stat.density, Geom.line, Geom.polygon(fill=true, preserve_order=true), alpha=[0.4]),
layer(Stat.quantile_bars(quantiles=[0.05, 0.95]), Geom.segment),
Guide.title("Density with bars showing the central 90% CI"),
Guide.ylabel("Density"), Coord.cartesian(xmin=-4, xmax=4)
)
x
-14
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
14
-12.0
-11.5
-11.0
-10.5
-10.0
-9.5
-9.0
-8.5
-8.0
-7.5
-7.0
-6.5
-6.0
-5.5
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
-20
-10
0
10
20
-12.0
-11.5
-11.0
-10.5
-10.0
-9.5
-9.0
-8.5
-8.0
-7.5
-7.0
-6.5
-6.0
-5.5
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
-1
1
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
-0.50
-0.45
-0.40
-0.35
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
-0.5
0.0
0.5
1.0
-0.50
-0.48
-0.46
-0.44
-0.42
-0.40
-0.38
-0.36
-0.34
-0.32
-0.30
-0.28
-0.26
-0.24
-0.22
-0.20
-0.18
-0.16
-0.14
-0.12
-0.10
-0.08
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
0.26
0.28
0.30
0.32
0.34
0.36
0.38
0.40
0.42
0.44
0.46
0.48
0.50
0.52
0.54
0.56
0.58
0.60
0.62
0.64
0.66
0.68
0.70
0.72
0.74
0.76
0.78
0.80
0.82
0.84
0.86
0.88
0.90
0.92
0.94
0.96
0.98
1.00
Density
Density with bars showing the central 90% CI
using DataFrames, Gadfly, RDatasets, Statistics
set_default_plot_size(21cm, 8cm)
salaries = dataset("car","Salaries")
salaries.Salary /= 1000.0
salaries.Discipline = ["Discipline $(x)" for x in salaries.Discipline]
df = combine(groupby(salaries, [:Rank, :Discipline]), :Salary.=>mean)
df.label = string.(round.(Int, df.Salary_mean))
p1 = plot(df, x=:Discipline, y=:Salary_mean, color=:Rank,
Scale.x_discrete(levels=["Discipline A", "Discipline B"]),
label=:label, Geom.label(position=:centered), Stat.dodge(position=:stack),
Geom.bar(position=:stack)
)
p2 = plot(df, y=:Discipline, x=:Salary_mean, color=:Rank,
Coord.cartesian(yflip=true), Scale.y_discrete,
label=:label, Geom.label(position=:right), Stat.dodge(axis=:y),
Geom.bar(position=:dodge, orientation=:horizontal),
Scale.color_discrete(levels=["Prof", "AssocProf", "AsstProf"]),
Guide.yticks(orientation=:vertical), Guide.ylabel(nothing)
)
hstack(p1, p2)
Salary_mean
-200
-150
-100
-50
0
50
100
150
200
250
300
350
-150
-140
-130
-120
-110
-100
-90
-80
-70
-60
-50
-40
-30
-20
-10
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
260
270
280
290
300
-200
0
200
400
-150
-145
-140
-135
-130
-125
-120
-115
-110
-105
-100
-95
-90
-85
-80
-75
-70
-65
-60
-55
-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
105
110
115
120
125
130
135
140
145
150
155
160
165
170
175
180
185
190
195
200
205
210
215
220
225
230
235
240
245
250
255
260
265
270
275
280
285
290
295
300
Prof
AssocProf
AsstProf
Rank
133
85
101
120
83
74
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
Discipline B
Discipline A
Discipline B
Discipline A
Discipline
Discipline A
Discipline B
Prof
AsstProf
AssocProf
Rank
133
85
101
120
83
74
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
-500
-400
-300
-200
-100
0
100
200
300
400
500
600
700
800
900
-400
-350
-300
-250
-200
-150
-100
-50
0
50
100
150
200
250
300
350
400
450
500
550
600
650
700
750
800
-500
0
500
1000
-400
-380
-360
-340
-320
-300
-280
-260
-240
-220
-200
-180
-160
-140
-120
-100
-80
-60
-40
-20
0
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
320
340
360
380
400
420
440
460
480
500
520
540
560
580
600
620
640
660
680
700
720
740
760
780
800
Salary_mean
using DataFrames, Gadfly
set_default_plot_size(14cm, 8cm)
sigmoid(x) = 1 ./ (1 .+ exp.(-x))
npoints = 30
gshift, x = rand([0,2], npoints), range(-9, 9, length=npoints)
y, ye = sigmoid(x+gshift), 0.2*rand(npoints)
df = DataFrame(x=x, y=y, ymin=y-ye, ymax=y+ye, g=gshift)
plot(y=[sigmoid, x->sigmoid(x+2)], xmin=[-10], xmax=[10],
Geom.line, Stat.func(100), color=[0,2], Guide.xlabel("x"),
layer(df, x=:x, y=:y, ymin=:ymin, ymax=:ymax, color=:g,
Geom.point, Geom.yerrorbar, Stat.x_jitter(range=1)),
Scale.color_discrete_manual("deepskyblue","yellow3", levels=[0,2]),
Guide.colorkey(title="Function", labels=["Sigmoid(x)", "Sigmoid(x+2)"]),
Theme(errorbar_cap_length=0mm, key_position=:inside)
)
x
-35
-30
-25
-20
-15
-10
-5
0
5
10
15
20
25
30
35
-30
-28
-26
-24
-22
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
-40
-20
0
20
40
-30
-29
-28
-27
-26
-25
-24
-23
-22
-21
-20
-19
-18
-17
-16
-15
-14
-13
-12
-11
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
9.0141046600311480.9998766054240137
8.6927540025953610.9999689322859989
8.1439459673205050.9995731370872474
7.5339262418893150.9992062365897446
6.6189131357520840.9998000496254946
5.547565393392760.9996281141773367
5.0757871084144220.999308435318234
4.3911606127179690.9987143089615129
4.1168057252619660.982612832728348
3.1354122445179220.9681328340340032
2.73212982377129960.9423020076914295
2.3910512889365950.8977447634316474
1.2028286926363470.9721241862548583
0.76812760129307690.9493594320914616
0.151905903768971230.5769694274020658
-0.249320961278248680.8441788063113398
-0.8281913982033450.7444001360376136
-1.58342552357296950.610229226597668
-2.10681175390071160.45700301154629586
-3.18894341318910430.05769799230857057
-3.2464350186055960.03186716596599677
-4.1491536301362420.017387167271651932
-4.6106589343894060.06567092533092088
-4.9267611518636330.036408609346018014
-5.8616504657392570.002741371708568133
-6.7756507565596650.010801164785603749
-7.01153531072300850.00583556787482027
-7.9328016384674130.00042686291275275794
-8.4381743082221340.00022951552431103904
-8.9822199994533580.0009110511944006454
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
Sigmoid(x)
Sigmoid(x+2)
Function
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
-4
-2
0
2
4
-2.5
-2.4
-2.3
-2.2
-2.1
-2.0
-1.9
-1.8
-1.7
-1.6
-1.5
-1.4
-1.3
-1.2
-1.1
-1.0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
3.0
3.1
3.2
3.3
3.4
3.5
y
using Distributions, Gadfly, RDatasets
set_default_plot_size(21cm, 8cm)
iris, geyser = dataset.("datasets", ["iris", "faithful"])
df = combine(groupby(iris, :Species), :SepalLength=>(x->fit(Normal, x))=>:d)
ds2 = fit.([Normal, Uniform], [geyser.Eruptions])
yeqx(x=4:6) = layer(x=x, Geom.abline(color="gray80"))
xylabs = [Guide.xlabel("Theoretical q"), Guide.ylabel("Sample q")]
p1 = plot(df, x=:d, y=iris[:,1], color=:Species, Stat.qq, yeqx(4:8),
xylabs..., Guide.title("3 Samples, 1 Distribution"))
p2 = plot(geyser, x=ds2, y=:Eruptions, color=["Normal","Uniform"], Stat.qq,
yeqx(0:6), xylabs..., Guide.title("1 Sample, 2 Distributions"),
Theme(discrete_highlight_color=c->nothing, alphas=[0.5], point_size=2pt)
)
hstack(p1, p2)
Theoretical q
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
14
16
18
-8.0
-7.5
-7.0
-6.5
-6.0
-5.5
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
15.5
16.0
-10
0
10
20
-8.0
-7.5
-7.0
-6.5
-6.0
-5.5
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
15.5
16.0
Normal
Uniform
Color
5.0870848708487085.067
5.0741697416974175.033
5.0612546125461265.0
5.0483394833948344.933
5.0354243542435424.933
5.022509225092254.933
5.0095940959409594.9
4.9966789667896684.8999999999999995
4.9837638376383764.883
4.9708487084870844.85
4.9579335793357924.833
4.9450184501845024.833
4.932103321033214.817
4.91918819188191854.817
4.9062730627306274.8
4.8933579335793364.8
4.8804428044280444.8
4.8675276752767534.8
4.8546125461254614.8
4.8416974169741694.8
4.8287822878228784.783
4.8158671586715874.767
4.8029520295202954.75
4.7900369003690034.732999999999999
4.7771217712177124.716000000000001
4.764206642066424.7
4.7512915129151294.7
4.7383763837638374.7
4.7254612546125454.7
4.71254612546125354.7
4.6996309963099634.7
4.68671586715867154.667
4.673800738007384.666999999999999
4.6608856088560894.650000000000001
4.6479704797047974.633
4.6350553505535064.633
4.6221402214022144.633
4.6092250922509224.617
4.596309963099634.6
4.5833948339483394.6
4.5704797047970484.6
4.5575645756457564.6
4.5446494464944644.583
4.5317343173431734.583
4.5188191881918824.583
4.505904059040594.583
4.4929889298892984.567
4.48007380073800654.567
4.46715867158671554.567
4.45424354243542454.549999999999999
4.4413284132841334.533
4.4284132841328414.533
4.4154981549815494.533
4.4025830258302594.533
4.3896678966789674.533
4.3767527675276754.517
4.3638376383763834.5
4.3509225092250924.5
4.33800738007384.5
4.3250922509225094.5
4.3121771217712174.5
4.2992619926199254.5
4.2863468634686344.5
4.2734317343173434.5
4.26051660516605154.4830000000000005
4.247601476014764.467
4.2346863468634694.467
4.2217712177121774.45
4.2088560885608864.45
4.1959409594095944.449999999999999
4.1830258302583024.433
4.1701107011070114.433
4.157195571955724.417
4.1442804428044284.417
4.1313653136531364.417
4.1184501845018454.417
4.1055350553505534.4
4.0926199261992624.383
4.079704797047974.367
4.0667896678966784.367
4.0538745387453874.367
4.04095940959409554.365999999999999
4.02804428044280454.3500000000000005
4.0151291512915134.35
4.0022140221402214.35
3.98929889298892974.35
3.97638376383763834.333
3.96346863468634644.333
3.95055350553505544.333
3.93763837638376354.333
3.9247232472324724.333
3.91180811808118064.317000000000001
3.8988929889298894.3
3.88597785977859774.3
3.87306273062730584.283
3.86014760147601484.283
3.8472324723247234.267
3.83431734317343144.267
3.821402214022144.25
3.80848708487084854.25
3.7955719557195574.25
3.78265682656826564.25
3.7697416974169744.2330000000000005
3.75682656826568234.233
3.74391143911439134.233
3.73099630996309944.2
3.7180811808118084.183
3.70516605166051654.167
3.6922509225092254.167
3.6793357933579334.167
3.66642066420664174.166999999999999
3.65350553505535074.150000000000001
3.6405904059040594.15
3.62767527675276744.15
3.6147601476014764.15
3.60184501845018454.133
3.58892988929889264.133
3.57601476014760164.117
3.56309963099630974.116999999999999
3.5501845018450184.1
3.53726937269372684.1
3.52435424354243534.083
3.5114391143911444.083
3.4985239852398524.083
3.4856088560885614.083
3.4726937269372694.083
3.4597785977859784.067
3.4468634686346864.066999999999999
3.43394833948339474.05
3.42103321033210334.033
3.40811808118081144.033
3.39520295202952044.000000000000001
3.38228782287822854.0
3.36937269372693754.0
3.35645756457564564.0
3.3435424354243544.0
3.33062730627306274.0
3.31771217712177123.967
3.304797047970483.966
3.2918819188191883.95
3.27896678966789653.95
3.2660516605166053.917
3.25313653136531363.917
3.2402214022140223.917
3.227306273062733.883
3.2143911439114393.85
3.20147601476014733.85
3.18856088560885633.8330000000000006
3.17564575645756443.833
3.1627306273062733.833
3.14981549815498153.833
3.13690036900368963.8329999999999997
3.12398523985239863.817
3.11107011070110673.767
3.09815498154981533.767
3.0852398523985243.75
3.07232472324723243.733
3.0594095940959413.7170000000000005
3.04649446494464953.683
3.0335793357933583.6
3.0206642066420663.6
3.00774907749077473.6
2.99483394833948323.6
2.9819188191881923.567
2.96900369003690033.5669999999999993
2.9560885608856093.5
2.94317343173431743.5
2.93025830258302563.450000000000001
2.91734317343173453.417
2.90442804428044273.367
2.8915129151291513.333
2.87859778597785983.333
2.8656826568265683.317
2.8527675276752773.067
2.8398523985239852.8999999999999995
2.8269372693726942.883
2.8140221402214022.800000000000001
2.80110701107011062.6330000000000022
2.7881918819188192.617
2.77527675276752772.483
2.76236162361623632.417
2.74944649446494442.417
2.7365313653136532.4
2.72361623616236152.4
2.710701107011072.3829999999999996
2.69778597785977862.367
2.6848708487084872.3500000000000005
2.67195571955719572.333
2.6590405904059042.317
2.6461254612546132.3
2.6332103321033212.2829999999999995
2.62029520295202942.267
2.6073800738007382.25
2.59446494464944652.25
2.5815498154981552.233
2.56863468634686362.233
2.5557195571955722.2170000000000005
2.54280442804428032.2
2.5298892988929892.2
2.51697416974169742.2
2.5040590405904062.183
2.49114391143911452.167
2.47822878228782262.167
2.46531365313653162.15
2.45239852398523972.133
2.43948339483394832.1
2.4265682656826572.1
2.41365313653136542.1
2.4007380073800742.083
2.3878228782287822.083
2.3749077490774912.067
2.3619926199261992.033
2.34907749077490772.033
2.33616236162361622.017
2.3232472324723252.017
2.31033210332103332.017
2.29741697416974142.0
2.284501845018452.0
2.27158671586715852.0
2.2586715867158672.0
2.24575645756457561.983
2.2328413284132841.983
2.21992619926199271.9829999999999999
2.20701107011070131.9670000000000003
2.194095940959411.967
2.1811808118081181.967
2.16826568265682651.95
2.1553505535055351.933
2.14243542435424361.933
2.1295202952029521.917
2.11660516605166031.9169999999999998
2.10369003690036931.883
2.09077490774907741.883
2.0778597785977861.883
2.06494464944649451.883
2.0520295202952031.867
2.03911439114391161.867
2.026199261992621.867
2.01328413284132871.867
2.0003690036900371.867
1.98745387453874531.867
1.97453874538745391.867
1.96162361623616241.867
1.9487084870848711.85
1.93579335793357931.85
1.92287822878228791.833
1.90996309963099641.833
1.89704797047970471.833
1.88413284132841331.833
1.87121771217712191.833
1.85830258302583041.833
1.84538745387453871.833
1.83247232472324731.817
1.81955719557195581.817
1.80664206642066421.817
1.79372693726937271.8
1.78081180811808131.8
1.76789667896678981.8
1.75498154981549811.8
1.74206642066420671.783
1.72915129151291521.783
1.71623616236162381.75
1.70332103321033211.75
1.69040590405904071.75
1.67749077490774921.75
1.66457564575645761.75
1.6516605166051661.75
1.63874538745387471.733
1.62583025830258321.7
1.61291512915129151.667
6.5401059126598185.067
6.2655669112023745.033
6.0943827937547115.0
5.96737024884597664.933
5.86528793030014.933
5.7793432161796754.933
5.7047500751407644.9
5.6386029069723414.8999999999999995
5.5789998638629124.883
5.5246254048191634.85
5.47453017303263154.833
5.4280056880042284.833
5.3845086178821834.817
5.3436127608382954.817
5.3049773680977914.8
5.2683255429860484.8
5.2334290897015854.8
5.20009762472898454.8
5.1681705843823954.8
5.1375112481151524.8
5.108002195005164.783
5.0795417986349374.767
5.05204148714744954.75
5.0254235757812034.732999999999999
4.99961953364376654.716000000000001
4.974568584006584.7
4.95021656370364.7
4.9265149859372254.7
4.9034202643115644.7
4.880893065800384.7
4.8588977676769334.7
4.8374019989129424.667
4.8163762506989024.666999999999999
4.7957935439037814.650000000000001
4.7756291437315124.633
4.7558603137274784.633
4.7364661027729924.633
4.7174271598773364.617
4.6987255725077884.6
4.6803447249426224.6
4.66226917373109554.6
4.6444845378295114.6
4.6269774013771374.583
4.6097352273987774.583
4.5927462809864874.583
4.57599956073234454.583
4.5594847373664234.567
4.5431920987059824.567
4.5271125001490324.567
4.5112373200522264.549999999999999
4.4955584194232464.533
4.4800681054340574.533
4.4647590983262874.533
4.4496245013352274.533
4.4346577733061634.533
4.4198527037173224.517
4.40520338985852264.5
4.3907042159447234.5
4.3763498339696574.5
4.3621351461273254.5
4.3480552886486854.5
4.3341056169180454.5
4.3202816917485344.5
4.3065792667091584.5
4.29299427640740654.4830000000000005
4.2795228256414654.467
4.2661611793450094.467
4.25290575325539954.45
4.2397531052430494.45
4.2266999272458914.449999999999999
4.2137430377583834.433
4.2008793748293054.433
4.1881059895269714.417
4.1754200398343644.417
4.16281878494012954.417
4.1502995798944894.417
4.1378598706019194.4
4.1254971891249194.383
4.1132091492754524.367
4.1009934424726694.367
4.0888478338473444.367
4.07677015857508354.365999999999999
4.064758318421914.3500000000000005
4.0528102784871094.35
4.0409240641295014.35
4.0290977580643754.35
4.0173294976193634.333
4.0056174721384184.333
3.99395992052392854.333
3.9823551289077254.333
3.9708014284424774.333
3.95929719320556564.317000000000001
3.94784083820813654.3
3.9364308175025474.3
3.92506562238191454.283
3.91374377966592534.283
3.9024638500674594.267
3.8912244266349824.267
3.8800241332659774.25
3.8688616232870384.25
3.85773557809649774.25
3.84664470586578134.25
3.83558774029588674.2330000000000005
3.82456343942563364.233
3.8135705844885544.233
3.8026079788154524.2
3.79167444677988734.183
3.78076883278396464.167
3.7698900002819884.167
3.7590368308396784.167
3.7482082232267724.166999999999999
3.73740309254096034.150000000000001
3.7266203693612184.15
3.7158589989287054.15
3.7051179403534814.15
3.69439616584540654.133
3.6836926599676494.133
3.6730064189113144.117
3.66233644978977064.116999999999999
3.65168176995133554.1
3.64104140630899934.1
3.63041439468596934.083
3.61979977917584074.083
3.609196611516244.083
3.59860395047485644.083
3.58802086124678484.083
3.5774464148621674.067
3.566879687603124.066999999999999
3.5563197604294.05
3.54576571840904764.033
3.53521665016150084.033
3.5246716472982724.000000000000001
3.51412980387430944.0
3.5035902158407684.0
3.49305198050113354.0
3.48251419596945374.0
3.47197596062981974.0
3.4614363725962783.967
3.4508945291723153.966
3.44034952630908643.95
3.4298004580615393.95
3.4192464160415873.917
3.40868648886746733.917
3.398119761608423.917
3.3875453152238023.883
3.3769622259957313.85
3.3663695649543473.85
3.35576639729474653.8330000000000006
3.3451517817846183.833
3.33452477016158833.833
3.32388440651925173.833
3.3132297266808163.8329999999999997
3.3025597575592733.817
3.29187351650293763.767
3.28117001062518073.767
3.27044823611710633.75
3.2597071775418823.733
3.2489458071093693.7170000000000005
3.2381630839296273.683
3.2273579532438153.6
3.21652934563090883.6
3.20567617618859923.6
3.19479734368662263.6
3.18389172969073.567
3.17295819765513533.5669999999999993
3.16199559198203333.5
3.15100273704495363.5
3.1399784361747013.450000000000001
3.1289214706048063.417
3.11783059837408953.367
3.10670455318354933.333
3.09554204320460973.333
3.08434174983560543.317
3.07310232640312763.067
3.0618223968046622.8999999999999995
3.05050055408867272.883
3.039135358968042.800000000000001
3.02772533826245072.6330000000000022
3.01626898326502162.617
3.00476474802811032.483
2.9932110475628622.417
2.98160625594665872.417
2.96994870433216862.4
2.95823667885122442.4
2.94646841840621182.3829999999999996
2.9346421123410862.367
2.92275589798347822.3500000000000005
2.91080785804867762.333
2.89879601789550372.317
2.88671834262324322.3
2.87457273399791772.2829999999999995
2.86235702719513572.267
2.85006898734566862.25
2.83770630586866762.25
2.82526659657609752.233
2.81274739153045732.233
2.80014613663622262.2170000000000005
2.78746018694361642.2
2.77468680164128272.2
2.76182313871220362.2
2.74886624922469652.183
2.7358130712275382.167
2.72266042321518682.167
2.70940499712557832.15
2.69604335082912262.133
2.68257190006318022.1
2.66898690976142872.1
2.65528448472205362.1
2.64146055955254242.083
2.62751088782190222.083
2.61343103034326242.067
2.59921634250092962.033
2.58486196052586432.033
2.5703627866120642.017
2.5557134727532652.017
2.5409084031644252.017
2.52594167513536052.0
2.51080707814430062.0
2.4954980710365312.0
2.4800077570473412.0
2.46432885641836071.983
2.4484536763215551.983
2.4323740777646041.9829999999999999
2.41608143910416431.9670000000000003
2.39956661573824271.967
2.38281989548411.967
2.36583094907181041.95
2.348588775093451.933
2.33108163864107581.933
2.31329700273949261.917
2.2952214515279651.9169999999999998
2.2768406039627981.883
2.2581390165932511.883
2.2391000736975951.883
2.2197058627431091.883
2.19993703273907531.867
2.1797726325668061.867
2.1591899257716861.867
2.1381641775576451.867
2.1166684087936541.867
2.09467311067020741.867
2.0721459121590231.867
2.04905119053336151.867
2.02534961276698631.85
2.00099759246400751.85
1.97594664282682091.833
1.9501426006893841.833
1.92352468932313811.833
1.89602437783565031.833
1.8675639814654271.833
1.83805492835543461.833
1.80739559208819231.833
1.7754685517416031.817
1.74213708676900181.817
1.70724063348453941.817
1.67058880837279621.8
1.6319534156322931.8
1.59105755858840371.8
1.54756048846636051.8
1.50103600343795551.783
1.45094077165142331.783
1.39656631260767481.75
1.33696326949824451.75
1.2708161013298241.75
1.19622296029091221.75
1.11027824617048761.75
1.00819592762460971.75
0.88118338271587811.733
0.70999926526821171.7
0.43546026381077231.667
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
-6.0
-5.5
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
-10
0
10
20
-6.0
-5.8
-5.6
-5.4
-5.2
-5.0
-4.8
-4.6
-4.4
-4.2
-4.0
-3.8
-3.6
-3.4
-3.2
-3.0
-2.8
-2.6
-2.4
-2.2
-2.0
-1.8
-1.6
-1.4
-1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
8.2
8.4
8.6
8.8
9.0
9.2
9.4
9.6
9.8
10.0
10.2
10.4
10.6
10.8
11.0
11.2
11.4
11.6
11.8
12.0
Sample q
1 Sample, 2 Distributions
Theoretical q
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
-5
0
5
10
15
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
8.2
8.4
8.6
8.8
9.0
9.2
9.4
9.6
9.8
10.0
10.2
10.4
10.6
10.8
11.0
11.2
11.4
11.6
11.8
12.0
12.2
12.4
12.6
12.8
13.0
13.2
13.4
13.6
13.8
14.0
setosa
versicolor
virginica
Species
8.1443160768333257.7
7.98153371220608057.7
7.8790715274783987.7
7.80248445937887.7
7.7405366305681417.6
7.6880813512981347.399999999999998
7.64231173771522257.3
7.6015209137885237.2
7.5645900179858117.2
7.53074408809550467.2
7.4994231881564137.1
7.4702089694365737.0
7.4427802468495466.9
7.4168848056209076.9
7.3923207876703326.9
7.368923989159476.9
7.3465589444852066.8
7.3251125144014366.8
7.3044891765631786.8
7.28460750169199766.7
7.2653974731711856.7
7.2467984180665566.7
7.2287573889257676.7
7.2112278829988166.7
7.19416881751862656.7
7.1775437017381256.7
7.1613199618866146.7
7.1454683862223276.600000000000001
7.1299626653134816.599999999999998
7.1147790085016516.5
7.0998958218129256.5
7.0852934358111536.5
7.0709538743309526.5
7.0568606568946766.5
7.04299862905643356.4
7.0293538160348116.4
7.0159132958721836.4
7.0026650890502496.4
6.9895980620410576.4
6.97670184271259956.4
6.9639667458620276.399999999999999
6.95138370743611046.3
6.9389442262319356.3
6.9266403120617826.3
6.9144644395231936.3
6.9024095066450236.3
6.8904687977880436.3
6.8786359502684386.3
6.8669049242477696.3
6.85526997549610156.3
6.8437256306883076.2
6.8322666649385976.2
6.8208880813166746.2
6.8095850921214296.199999999999998
6.7983531017160316.1
6.7871876907520536.1
6.7760846016308036.1
6.7650397250676346.1
6.7540490876402556.1
6.7431088402151546.1
6.73221524715768556.0
6.7213646762411876.0
6.710553589179066.0
6.6997785327111796.0
6.6890361301824346.0
6.6783230735567696.0
6.6676361158149585.9
6.6569720636884885.9
6.6463277706855385.9
6.6357001303680785.8
6.6250860698417295.8
6.61448254342217555.8
6.6038865264436965.8
6.5932950091768235.8
6.5827049908231735.8
6.5721134735563015.8
6.5615174565778215.7
6.5509139301582675.7
6.5402998696319195.7
6.5296722293144595.7
6.5190279363115095.7
6.5083638841850385.7
6.4976769264432275.7
6.4869638698175625.7
6.4762214672888175.6
6.4654464108209375.6
6.454635323758815.6
6.4437847528423115.6
6.4328911597848435.6
6.4219509123597415.6
6.4109602749323625.5
6.3999153983691945.5
6.3888123092479435.5
6.3776468982839665.5
6.3664149078785685.5
6.3551119186833225.5
6.34373333506139855.5
6.332274369311695.4
6.3207300245038955.4
6.3090950757522285.4
6.2973640497315585.4
6.2855312022119545.4
6.2735904933549735.4
6.2615355604768045.300000000000001
6.2493596879382155.2
6.2370557737680625.2
6.2246162925638865.2
6.2120332541379695.2
6.1992981572873975.1
6.186401937958945.1
6.1733349109497475.1
6.1600867041278145.1
6.1466461839651865.1
6.1330013709435635.1
6.1191393431053215.1
6.1050461256690455.1
6.0907065641888445.099999999999999
6.0761041781870715.0
6.0612209914983455.0
6.0460373346865165.0
6.0305316137776695.0
6.0146800381133835.0
5.9984562982618715.0
5.981831182481375.0
5.964772117001185.0
5.947242611074235.0
5.9292015819334415.0
5.9106025268288124.9
5.8913924983079994.9
5.8715108234368194.9
5.8508874855985614.9
5.829441055514794.9
5.8070760108405284.9
5.7836792123296644.8
5.7591151943790894.8
5.73321975315045054.8
5.7057910305634234.8
5.6765768118435834.8
5.6452559119044924.7
5.6114099820141864.7
5.5744790862114744.6
5.5336882622847744.6
5.48791864870186254.6
5.4354633694318564.6
5.3735155406211984.5
5.2969284725215984.4
5.1944662877939174.4
5.0316839231666734.4
7.199329509343597.7
7.0671919777100667.7
6.9840189619678677.7
6.9218499047920287.7
6.8715641554245167.6
6.8289839217501997.399999999999998
6.791830733955477.3
6.7587190464620837.2
6.72874063065789057.2
6.7012664159799827.2
6.6758418780334477.1
6.65212742494093157.0
6.6298623367858986.9
6.6088418798564956.9
6.5889021971616096.9
6.5699099972975026.9
6.5517553169370236.8
6.5343463160289116.8
6.5176054549274536.8
6.5014666339425516.7
6.485873017538456.7
6.4707753548532496.7
6.4561306661349646.7
6.4419012030770546.7
6.4280536170090636.7
6.414558286803076.7
6.4013887709113936.7
6.3885213568915936.600000000000001
6.3759346882325836.599999999999998
6.36360945302126356.5
6.3515281224889756.5
6.3396747300981426.5
6.32803468381278666.5
6.3165946057117686.5
6.3053421942716546.4
6.2942661055540166.4
6.2833558502433316.4
6.27260170404316.4
6.26199462938401656.4
6.25152620675501156.4
6.2411885742553126.399999999999999
6.2309743741983356.3
6.2208767057875996.3
6.2108890830399046.3
6.2010053972584756.3
6.1912198834641726.3
6.18152709028028556.3
6.1719218528393826.3
6.1623992683416726.3
6.1529546739456616.3
6.1435836267150766.2
6.1342818853826736.2
6.1250453937225996.2
6.1158702653494476.199999999999998
6.1067527697847456.1
6.0976893196509886.1
6.0886764588699786.1
6.079710851756486.1
6.0707892729106486.1
6.061908597823256.1
6.0530657941170256.0
6.044257913355476.0
6.0354820833573266.0
6.0267355009610216.0
6.0180154251886086.0
6.0093191707632166.0
6.0006441019379875.9
5.9919876265978625.9
5.9833471905984555.9
5.9747202723087885.8
5.9661043773267235.8
5.9574970333377165.8
5.9488957850889315.8
5.9402981894519395.8
5.9317018105480615.8
5.9231042149110695.8
5.9145029666622845.7
5.9058956226732775.7
5.89727972769121145.7
5.88865280940154455.7
5.88001237340213745.7
5.8713558980620135.7
5.8626808292367845.7
5.8539845748113925.7
5.8452644990389785.6
5.83651791664267355.6
5.82774208664452955.6
5.8189342058829755.6
5.810091402176755.6
5.8012107270893525.6
5.792289148243525.5
5.7833235411300225.5
5.7743106803490125.5
5.7652472302152555.5
5.7561297346505525.5
5.7469546062774015.5
5.7377181146173275.5
5.7284163732849245.4
5.7190453260543395.4
5.7096007316583285.4
5.70007814716061745.4
5.6904729097197145.4
5.6807801165358285.4
5.6709946027415255.300000000000001
5.6611109169600965.2
5.6511232942124015.2
5.64102562580166565.2
5.6308114257446885.2
5.6204737932449895.1
5.6100053706159835.1
5.59939829595695.1
5.5886441497566695.1
5.5777338944459845.1
5.5666578057283465.1
5.5554053942882325.1
5.5439653161872135.1
5.53232526990185755.099999999999999
5.5204718775110255.0
5.5083905469787365.0
5.4960653117674175.0
5.4834786431084075.0
5.4706112290886075.0
5.457441713196935.0
5.4439463829909375.0
5.43009879692294555.0
5.4158693338650365.0
5.4012246451467515.0
5.386126982461554.9
5.3705333660574494.9
5.3543945450725474.9
5.3376536839710894.9
5.3202446830629774.9
5.30209000270249754.9
5.2830978028383914.8
5.2631581201435054.8
5.2421376632141024.8
5.2198725750590684.8
5.1961581219665534.8
5.17073358402001754.7
5.1432593693421094.7
5.1132809535379174.6
5.080169266044534.6
5.0430160782498014.6
5.0004358445754854.6
4.9501500952079734.5
4.8879810380321324.4
4.8048080222899354.4
4.672670490656414.4
5.8687189375638397.7
5.7784831360092527.7
5.7216848618896417.7
5.6792300450272117.7
5.644890256438347.6
5.61581251093723657.399999999999998
5.5904408573311777.3
5.5678291161789557.2
5.54735706447656357.2
5.5285951143107347.2
5.5112328741326497.1
5.4950384390105617.0
5.4798337651263466.9
5.4654790412517616.9
5.4518623705869326.9
5.43889272935906656.9
5.4264950243766656.8
5.4146065387073186.8
5.4031743210661276.8
5.39215323243434156.7
5.3815044602197876.7
5.3711943713513326.7
5.36119361525526.7
5.351476413875336.7
5.3420199936365536.7
5.3328041264765786.7
5.3238107556463496.7
5.3150236880838066.600000000000001
5.3064283395760276.599999999999998
5.2980115221518396.5
5.2897612655370086.5
5.2816666662940446.5
5.2737177596230416.5
5.2659054098346756.5
5.2582212163041286.4
5.25065743233472756.4
5.2432068948458396.4
5.2358629631829986.4
5.2286194656529686.4
5.2214706526301826.4
5.214411155277276.399999999999999
5.2074359490812256.3
5.2005403215361146.3
5.1937198434091076.3
5.18697034311365456.3
5.1802878837855866.3
5.1736687427176526.3
5.1671093928577966.3
5.1606064861181336.3
5.1541568382766286.3
5.1477574152829926.2
5.1414053208053176.2
5.1350977848751746.2
5.12883215350698856.199999999999998
5.1226058791829526.1
5.1164165121079136.1
5.1102616921500986.1
5.1041391413932576.1
5.0980466572342616.1
5.0919821059674816.1
5.08594341680357156.0
5.0799285762757466.0
5.0739356229913946.0
5.0679626426909696.0
5.06200776357969056.0
5.0560691519006546.0
5.0501450077206585.9
5.0442335609023535.9
5.0383330672382975.9
5.0324418047242295.8
5.02655806995027455.8
5.020680174590025.8
5.01480644196837755.8
5.0089352036899315.8
5.0030647963100685.8
4.9971935580316215.8
4.9913198254099785.7
4.9854419300497245.7
4.979558195275775.7
4.9736669327617015.7
4.96776643909764555.7
4.961854992279345.7
4.9559308480993455.7
4.9499922364203085.7
4.9440373573090295.6
4.9380643770086055.6
4.9320714237242535.6
4.9260565831964275.6
4.9200178940325185.6
4.9139533427657385.6
4.9078608586067425.5
4.9017383078499015.5
4.8955834878920865.5
4.8893941208170465.5
4.883167846493015.5
4.8769022151248255.5
4.8705946791946815.5
4.8642425847170065.4
4.8578431617233715.4
4.8513935138818665.4
4.8448906071422035.4
4.8383312572823475.4
4.8317121162144135.4
4.8250296568863445.300000000000001
4.8182801565908925.2
4.8114596784638845.2
4.8045640509187735.2
4.7975888447227285.2
4.7905293473698175.1
4.7833805343470315.1
4.7761370368175.1
4.768793105154165.1
4.7613425676652715.1
4.75377878369587055.1
4.7460945901653245.1
4.7382822403769575.1
4.7303333337059545.099999999999999
4.7222387344629915.0
4.713988477848165.0
4.70557166042397155.0
4.6969763119161935.0
4.688189244353655.0
4.6791958735234215.0
4.6699800063634465.0
4.6605235861246685.0
4.6508063847447995.0
4.6408056286486675.0
4.63049553978021144.9
4.6198467675656574.9
4.6088256789338714.9
4.5973934612926814.9
4.58550497562333354.9
4.5731072706409324.9
4.5601376294130674.8
4.5465209587482374.8
4.5321662348736534.8
4.5169615609894374.8
4.500767125867354.8
4.4834048856892644.7
4.4646429355234354.7
4.4441708838210444.6
4.4215591426688224.6
4.3961874890627624.6
4.3671097435616594.6
4.3327699549727884.5
4.2903151381103574.4
4.2335168639907474.4
4.1432810624361594.4
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
0
5
10
15
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
8.2
8.4
8.6
8.8
9.0
9.2
9.4
9.6
9.8
10.0
10.2
10.4
10.6
10.8
11.0
11.2
11.4
11.6
11.8
12.0
Sample q
3 Samples, 1 Distribution
using Compose, Gadfly, RDatasets
set_default_plot_size(21cm,8cm)
salaries = dataset("car","Salaries")
salaries.Salary /= 1000.0
salaries.Discipline = ["Discipline $(x)" for x in salaries.Discipline]
p = plot(salaries[salaries.Rank.=="Prof",:], x=:YrsService, y=:Salary,
color=:Sex, xgroup = :Discipline,
Geom.subplot_grid(Geom.point,
layer(Stat.smooth(method=:lm, levels=[0.95, 0.99]), Geom.line, Geom.ribbon)),
Scale.xgroup(levels=["Discipline A", "Discipline B"]),
Guide.colorkey(title="", pos=[0.43w, -0.4h]),
Theme(point_size=2pt, alphas=[0.5])
)
YrsService by Discipline
Discipline B
Discipline A
-70
-60
-50
-40
-30
-20
-10
0
10
20
30
40
50
60
70
80
90
100
110
120
130
-60
-55
-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
105
110
115
120
-100
0
100
200
-60
-58
-56
-54
-52
-50
-48
-46
-44
-42
-40
-38
-36
-34
-32
-30
-28
-26
-24
-22
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
66
68
70
72
74
76
78
80
82
84
86
88
90
92
94
96
98
100
102
104
106
108
110
112
114
116
118
120
-70
-60
-50
-40
-30
-20
-10
0
10
20
30
40
50
60
70
80
90
100
110
120
130
-60
-55
-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
105
110
115
120
-100
0
100
200
-60
-58
-56
-54
-52
-50
-48
-46
-44
-42
-40
-38
-36
-34
-32
-30
-28
-26
-24
-22
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
66
68
70
72
74
76
78
80
82
84
86
88
90
92
94
96
98
100
102
104
106
108
110
112
114
116
118
120
21,145.028
20,138.0
27,142.5
38,93.519
49,186.96
28,144.309
33,128.25
27,142.023
10,107.986
35,150.376
31,162.15
38,114.596
17,124.312
11,106.231
15,137.317
25,128.464
12,145.0
23,98.053
38,151.445
19,145.098
10,105.45
9,116.518
60,192.253
23,134.778
37,151.65
15,124.714
31,162.221
15,161.101
23,104.428
15,135.027
16,134.55
45,67.559
20,129.6
10,145.2
21,170.0
11,119.5
11,146.0
11,145.35
19,126.2
7,128.4
39,111.35
20,163.2
18,120.0
33,162.2
2,96.545
5,165.0
17,152.5
17,160.4
40,119.7
22,114.5
33,189.409
18,122.1
22,133.7
9,180.0
19,153.75
10,107.5
30,134.0
23,101.0
22,150.0
5,141.136
11,142.467
14,147.349
25,111.751
20,134.185
24,93.164
19,151.575
18,181.257
19,130.664
16,167.284
8,105.89
19,176.5
16,137.167
21,118.971
9,111.168
12,128.148
26,144.651
27,156.938
28,119.015
27,112.696
14,127.512
5,153.303
23,126.933
25,133.217
26,106.689
14,102.235
7,129.676
20,123.683
38,166.024
25,172.272
37,152.708
14,132.825
18,122.96
20,144.64
16,135.585
28,150.743
19,193.0
3,150.48
23,113.398
34,92.391
19,100.131
45,146.856
2,126.32
36,91.412
17,111.512
31,99.418
12,101.0
31,109.785
21,117.704
9,106.639
11,108.875
28,126.621
25,140.096
19,151.768
28,98.193
15,114.778
19,94.384
38,231.545
27,101.299
2,146.5
31,125.196
21,155.75
9,117.256
4,132.261
8,118.223
20,101.0
3,117.15
18,104.8
18,129.0
20,119.25
45,147.765
23,175.0
41,141.5
39,115.0
16,173.2
18,139.75
15,95.329
25,101.738
19,150.564
30,103.106
19,151.292
19,166.605
18,186.023
36,119.45
15,109.305
27,139.219
9,114.33
21,125.192
44,105.0
23,172.505
38,150.68
26,103.649
19,103.275
26,136.66
7,109.707
20,110.515
31,134.69
30,131.95
10,115.435
40,101.036
43,205.5
30,138.771
15,109.646
11,121.946
14,109.954
35,107.309
40,88.709
6,146.8
35,100.351
19,94.35
9,108.1
7,92.05
15,166.8
28,122.5
31,111.35
44,144.05
4,105.26
13,170.5
16,127.1
36,88.6
43,72.3
11,148.8
18,126.3
36,97.15
7,107.3
9,183.8
28,168.5
7,174.5
27,150.5
27,115.8
43,155.865
51,57.8
27,103.6
38,136.5
46,100.6
18,107.1
27,163.2
48,107.2
6,93.0
18,194.8
40,143.25
7,103.7
44,89.65
10,104.35
43,143.94
30,134.8
35,99.0
31,126.0
26,121.2
45,107.55
30,92.55
22,140.3
7,116.45
12,132.0
8,102.0
39,109.0
7,204.0
23,128.8
18,101.1
23,91.1
11,90.45
23,84.273
23,108.2
37,102.6
30,122.875
6,96.2
40,77.202
25,114.0
17,81.7
19,117.555
19,148.75
27,91.0
38,133.9
11,88.175
20,122.4
35,87.8
11,106.608
18,152.664
14,105.668
14,108.262
18,136.0
25,168.635
57,76.84
30,113.278
26,155.5
49,78.162
22,96.614
22,97.262
32,124.309
14,115.313
36,117.515
29,148.5
9,120.806
0,105.0
37,104.279
16,112.429
31,131.205
28,113.543
23,134.885
8,106.294
19,113.068
30,93.904
31,102.58
26,89.565
36,137.0
23,124.75
34,103.45
-300
-250
-200
-150
-100
-50
0
50
100
150
200
250
300
350
400
450
500
550
-260
-240
-220
-200
-180
-160
-140
-120
-100
-80
-60
-40
-20
0
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
320
340
360
380
400
420
440
460
480
500
-250
0
250
500
-250
-240
-230
-220
-210
-200
-190
-180
-170
-160
-150
-140
-130
-120
-110
-100
-90
-80
-70
-60
-50
-40
-30
-20
-10
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
260
270
280
290
300
310
320
330
340
350
360
370
380
390
400
410
420
430
440
450
460
470
480
490
500
Male
Female
Salary
using DataFrames, Gadfly
set_default_plot_size(14cm, 8cm)
x = range(0.1, stop=4.9, length=30)
D = DataFrame(x=x, y=x.+randn(length(x)))
p = plot(D, x=:x, y=:y, Geom.point,
layer(Stat.smooth(method=:lm, levels=[0.90,0.99]), Geom.line, Geom.ribbon(fill=false)),
Theme(lowlight_color=c->"gray", line_style=[:solid, :dot])
)
x
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
-5
0
5
10
-5.0
-4.8
-4.6
-4.4
-4.2
-4.0
-3.8
-3.6
-3.4
-3.2
-3.0
-2.8
-2.6
-2.4
-2.2
-2.0
-1.8
-1.6
-1.4
-1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
8.2
8.4
8.6
8.8
9.0
9.2
9.4
9.6
9.8
10.0
4.95.1236639415945415
4.734482758620694.0857910741253916
4.5689655172413794.842611204779619
4.4034482758620695.195002504701055
4.23793103448275853.4375754021151135
4.0724137931034484.107922059526255
3.9068965517241383.992767976624603
3.74137931034482743.1102867960007323
3.5758620689655171.8598553056163665
3.4103448275862073.506423176253704
3.24482758620689673.888036205840213
3.0793103448275863.7097949046331724
2.9137931034482764.751394901784543
2.74827586206896561.8294619728000718
2.58275862068965531.9857062888307766
2.41724137931034472.489270577398729
2.25172413793103441.8653621575453705
2.0862068965517241.8320121533789155
1.92068965517241372.882931073697085
1.75517241379310350.3927434572513049
1.5896551724137931.1669398573016456
1.42413793103448282.1121293554397504
1.25862068965517240.8253280905880598
1.09310344827586210.1632698913543228
0.9275862068965517-0.7137276968158517
0.76206896551724131.7868092494125287
0.5965517241379310.6064197782701288
0.43103448275862066-1.0777001238387638
0.2655172413793103-0.3182382032287451
0.1-0.2225696498949116
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
14
16
-10.0
-9.5
-9.0
-8.5
-8.0
-7.5
-7.0
-6.5
-6.0
-5.5
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
-10
0
10
20
-10.0
-9.5
-9.0
-8.5
-8.0
-7.5
-7.0
-6.5
-6.0
-5.5
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
y
using Gadfly, Random
set_default_plot_size(14cm, 8cm)
Random.seed!(1234)
plot(x=rand(25), y=rand(25), Stat.step, Geom.line)
x
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
-1.0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
-1
0
1
2
-1.00
-0.95
-0.90
-0.85
-0.80
-0.75
-0.70
-0.65
-0.60
-0.55
-0.50
-0.45
-0.40
-0.35
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1.05
1.10
1.15
1.20
1.25
1.30
1.35
1.40
1.45
1.50
1.55
1.60
1.65
1.70
1.75
1.80
1.85
1.90
1.95
2.00
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
-1.0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
-1
0
1
2
-1.00
-0.95
-0.90
-0.85
-0.80
-0.75
-0.70
-0.65
-0.60
-0.55
-0.50
-0.45
-0.40
-0.35
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1.05
1.10
1.15
1.20
1.25
1.30
1.35
1.40
1.45
1.50
1.55
1.60
1.65
1.70
1.75
1.80
1.85
1.90
1.95
2.00
y
using Gadfly, Distributions, Random
set_default_plot_size(14cm, 8cm)
Random.seed!(1234)
plot(x=rand(1:4, 500), y=rand(500), Stat.x_jitter(range=0.5), Geom.point)
x
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
-5
0
5
10
-5.0
-4.8
-4.6
-4.4
-4.2
-4.0
-3.8
-3.6
-3.4
-3.2
-3.0
-2.8
-2.6
-2.4
-2.2
-2.0
-1.8
-1.6
-1.4
-1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
8.2
8.4
8.6
8.8
9.0
9.2
9.4
9.6
9.8
10.0
2.8712597494860850.8688707602114859
3.90884067575680570.7844962241285454
4.2014967064336270.4933114676059782
2.8761044222740660.20022370872836803
2.92891828508707340.20968130045034283
2.78390869002773520.38636188269952176
4.1540847037771450.20347016439928334
2.06530122447205060.36346714080005693
3.9750557120175140.9006551405197767
4.2069651933828380.5601212750115374
4.1590095831739340.41599712143214107
2.81509726300803550.06337029084489987
2.2191516662950040.25571730367793133
1.89561480552621390.8394899341730585
2.89572224818001980.4722104397712146
3.1619889707102110.8994205095858897
2.02793302385898460.7098322716968657
2.243811014935740.7032030724629521
2.0287919878980740.8587698550547092
4.055445782623950.8147518729572419
2.0208082508583720.17645290580491135
1.9879890479051410.5218498496945619
0.92389981202464520.397287897949688
1.98544329088483670.08241379533623139
2.1778156681076440.22152131696852717
3.2132729454540690.2434992488546217
1.8649664072617470.30065587786042136
4.1683785836592610.489277411012798
1.88764125329319230.06178649155507521
2.17026487372069270.8817395195040358
3.92780526883187870.23362897774250269
0.87650630409778250.8255340069167519
4.0432409163642910.9105974192931239
4.2452746207158590.5991438659250566
4.1033452351789710.1412748986274308
3.8062302679794830.7366810973361577
1.24196582016799750.43709223813367404
1.98160899261812860.6854790284282768
0.99671323218591520.9911127611685743
1.9074460439983090.6534693194895459
4.0600616252240170.6808836785816833
3.9697029433718960.19952370079780724
0.89088942250721110.4324597305101038
4.010239234022630.3705886993148091
4.1274456192291350.8513118835602637
2.86943155365610950.06533462152067271
1.80970218924360050.7622851899438685
1.85733649626418760.9305245964744348
1.92345128181091420.04526321728496874
4.0450377724757770.2540701150733844
3.9834566375209430.9389170955706365
2.75509057464444450.5744689301853781
1.12177867649153540.6401419378013135
2.04193069906356730.05136288720653859
0.85027402562036150.1115511154107186
2.87134088666257540.7654791906083002
3.78273713325283630.7569242593203886
3.0097053698180690.10043917697495508
2.9565868376540050.9180092814287552
1.9675850238235890.4519085995436555
2.09102600347075860.4966565997490733
3.9307148236662650.8496701726979725
3.88371962759620940.2797556635344246
3.16148151157757740.9031209367973443
1.20282767950029370.8337379298530495
3.8384762610623510.9202623263285435
4.1677016255738740.14752969866227228
3.99624935611840070.6203029982311218
1.752903313769290.6412455362756176
1.91263109801300860.8633707470087496
0.76611426760085030.5513297262619098
1.84703325897522050.7213181391973645
1.7989699747854320.5565117796386155
2.011312561262340.562391983355749
3.84753065878414050.8332854442345888
2.91438850069447140.683847959065649
1.93870405601566050.40434526102519175
2.8810009279871550.30621765112578814
4.22655434374681250.3822671853075492
2.1848433077145990.47861038568821057
4.2315706852444230.3913377406425659
1.82208498084682270.8528930050931623
0.86899031204470820.1910058465303195
0.95942749481530730.9319449966435072
2.84428701791341250.7301492979311955
1.75462250809559480.1716937223218291
1.76502516534107310.10540966113139583
1.800186637053180.14221287858362786
3.8071183858186140.5799623818434465
3.87923523330139370.8582735063047109
0.97241595342174250.9737954714715884
1.1017946678675860.04883640354012009
1.19713854311161620.9025551644489788
3.218302950602240.27986574868940217
2.78546548448190650.642259389228578
1.13450336129567340.7140000771545962
3.84680916021972760.5514339758243468
1.95121809777027620.41002221928315785
0.82290465864745440.4791719547087512
3.1006933357493220.6068327793530726
0.86969428029971290.8284301938432143
1.19922188161121350.06532217208669655
2.22736240427727860.9842933037760484
1.93162081377750280.7455808217291413
1.03415233755323980.6831405194880031
3.0269582457217740.9193478032857196
2.13943155066927960.05251671833846783
1.1316937106082710.30417155724859757
1.21865539842861730.7649296155316312
3.8870914479729310.54298931744888
3.0889825290383950.4698473493890444
3.0794431255458820.9624535633440595
0.79256522507845820.05938962971774009
4.09311226600018950.2892787961097918
3.1886865362168980.5824468146224191
2.96152542022626040.4339580706911712
2.1122555090557410.23829837438298052
1.99320088674342570.6802437761705059
2.0239655411868220.9758045059138829
1.77044912552940280.07026486226463047
1.87400100539144780.631724907029242
2.0199155646577680.11294303759673174
3.82079098584652770.6662179679029121
2.15462638955604020.1354497892449471
3.91114083630068080.37368905687339526
3.8720887497329750.6845749899644828
3.17442489271927550.13480216534545464
0.97250252264953090.26406861992335895
3.1008535597066160.8927396362839568
1.80059745657557620.03171441883005599
3.0028938815546660.849443710514651
1.88423177371639120.40853753061840636
1.02637636823164470.28294823714123485
2.0456276117348970.8231516374761443
1.9741197444118650.9560767968343468
1.13255056395567970.04661289240658861
3.96006212249316470.8668717472509425
1.02712689926106910.7144283934013788
0.98883464374242860.8400089067709939
1.2429428775124390.27494095179421774
0.85721121174327640.14283994035433067
3.9925418747703830.06193096369350859
2.9458808949474390.5543013918697022
2.8835642123430240.36781387880660465
0.97029987680635120.9409660265169163
2.12970652340058160.24735507677280189
4.1054537620315130.27286009818548573
3.1130142760270520.5130113748104197
2.87077110906373620.810095349938354
2.161179913117280.039244941424736335
2.07968833532424920.24890378913702726
2.7696471418158620.1751722300394265
1.804715297518420.0005917711362134481
4.1824049141869590.3719608058921303
4.072811924714030.058608492349195074
2.799770521768430.12011628613261238
1.76617074870835690.28143985356705925
2.24764273225316740.6202785432039485
2.2122079485985620.8700357516595604
3.91652606674083170.019972582532043703
0.93630187081043680.987908302749377
1.16122887372639070.7552467868535593
0.81701607493336560.5140433713604216
1.14901522248591470.7021756418052602
4.1909122118898990.18019934546680438
2.8464596710996580.46430881182502726
4.0631968498540790.27596643355398653
2.05868538795040120.031779235808084394
2.081024673632640.5029323603596401
2.0999857224716020.1322055312857282
0.8552997943745170.22624225698362654
2.03311159069952250.9901222460263335
1.86721261243744860.7018230289433646
2.93402131313415550.5483182356088243
2.07903629687126120.6851882186499314
2.083621290435720.25554705343485484
3.9796375197535250.8040460964694363
0.89539294224921820.23172871039837373
1.75199354453743350.39033820071475933
1.03444993669967490.04935999784699163
2.05322203265875560.5270047985994559
1.97819736502428340.12633860607778602
1.13669672982365680.6859244357507125
3.9458543430417380.9347001577624441
3.1388120129138190.6289955692908717
2.0454204894301640.4156080283059055
2.0053109149752350.4824761299463617
4.1426164442304880.7527410357550373
3.11457706661103150.1364677260308763
3.94017389289840820.5243925284587394
0.89288618288190950.8887742373097278
4.121528193386090.5973158992545364
0.86072132136157380.185888775682863
2.1639561594030930.08119296696646194
3.8314324897182210.8543586741980717
3.9718487515771610.43967094523384076
4.2118995781614010.857783472261158
4.2167424957096340.7620738638659255
2.1482293600074480.297193812375811
0.76643541622001920.6964472695956948
1.20245470901159470.8444267400793738
0.9569839052291050.1825057607253029
2.93062612877669970.5650379859078629
2.98379658262446770.6890488323646001
1.20847000285129710.7444158227835773
3.08724331754477980.9070250703704628
3.19818970901706660.7251897791927844
4.1087312021257710.9582538153032606
4.0117027638056480.7372812787327768
0.95387834184928060.15461746682785849
0.92177887686691240.6128807362063043
0.97074541895466850.260988098859801
4.21224227345058560.36597531632541225
2.89053643566361630.27638995986758275
1.06966771210481460.02853550112696346
3.97829491712853760.26623330759253705
2.9343597791690650.8822263611775025
1.0082259940667550.7797272162266058
1.24202612733899450.6723370448480452
4.0667298061719250.7189174328788034
1.09679460442816910.9104012565181274
2.2438175681615960.22814256947908007
4.0975178179132080.2926767210770894
1.80398564941876140.5335158113802787
3.91228646284267970.5224652349864496
0.85248094165445070.1854992374002269
1.92468672450880350.4313936472999286
3.9963221729939510.23163315976888965
0.98227644653067250.008941785599641316
1.01861978680671220.8801608411211824
0.90583025207289420.9495904988651728
3.12259010944273150.7251610128749185
0.76079710981379060.7653749686121726
2.04123089316883230.3889837880913415
3.9659071571322830.33454205519933766
1.17208693899642480.5409635231684314
1.23557494130732740.5417859419322087
1.76399132393748430.6995561236538012
2.0933754508327590.9768058652920141
3.07325747479750080.07157621671962944
1.17128656677569330.21095849801002242
1.79129309903202530.24484660314257278
1.09647728717019820.26819716438786256
3.04203340907237860.7607715755994937
1.0340684248389440.8443210731301258
3.82317726781159270.31529067609398276
2.86193123065980660.6484925911208542
3.24706716889914840.3685559216792129
0.94654552932500910.6205061868459367
3.92781475263438340.9523401569298326
2.06931864543716730.5187125306836177
4.2006977907464450.5916756129971316
3.17164003488570150.8101677940907962
3.9444791704311640.24882164172116517
1.17115188753051620.3039008559181663
1.11769242098911770.5730465638219848
4.21288391298796850.7172403284161356
0.96605697709104510.14386756622287056
1.93832610798619640.695696967985678
2.76771692359299060.5239610185536518
2.21875647328071770.5214146727674995
1.81757341803903080.19316006943534714
3.80452405450155150.8443182452610154
0.77215648756982250.04817127912951269
1.03634253106889070.0015749053225861953
4.1024404353448190.6176051821237512
4.2193731760611370.934486725639464
2.0961057900728310.215287910639833
2.98186568437307950.520000055822197
0.75909106962863750.8925689467243849
2.78964403448908360.27168082454940823
2.9925193197182150.5392913071429981
0.91955592853326070.5535703272043874
2.0704230829243950.66064841597728
2.9166028640134110.3669039349529569
3.8797079924654070.9749001931944528
3.19049261002831530.762504522777882
2.11090097913095050.520930941494485
0.97094766332092910.16272321889578512
2.20420085162979660.9537990881567351
1.19274320690306410.8363896294256541
3.21166583451170240.8682005168268859
3.06925487175301060.547207184165385
4.1077255897820470.925886679280969
2.75901256220109040.901074525624822
1.88451076785885550.3117414903705903
1.81134539331395740.6642422457423811
2.97776347688545240.5810909916932919
3.0643465224393010.8537548518511688
3.03469608703741270.46039783867502093
3.12057034794669260.9929498020696101
2.83617722602818740.6113158849330347
0.89789039109250890.58230855642697
3.0358075963978180.5296498813973983
1.0230433550643360.7490827228368587
2.0793864025271120.7000017994857771
3.11782310996961340.9036221496963194
1.00346683089635040.942588189733702
4.21516219142494550.9755806406492921
1.79223188928033820.7333388252259753
3.7577401019838910.5845543847866221
0.78615954455866980.7656854824149089
3.185063129435120.05537909946775932
2.75367379137047850.050307398032391704
2.83001953374771140.5148028287268799
4.127429892159790.7353569902097455
2.0620754209140630.6430375271161651
2.99506679619945440.36263693424831744
3.2071112073218610.05221968494849505
3.83247985161245230.33102535353292806
3.98197962066977860.1880083620572911
3.95644831148749930.5338124396263914
3.00149126495921740.5107420498787975
0.89089098010358680.7214471968410925
3.0644364214865650.255432407815942
1.978581400534030.3851255814540926
2.8622185278532910.8238123963398689
3.7770851181711870.7568890891259715
3.78358957965905240.41316557081364846
0.88276980287178860.20650303966258898
0.90740469371833190.8630007726329422
4.1121116870443220.5221762619529627
4.037552151920550.4159443871114292
2.15685602864908080.8501629612033895
4.0238882527834560.9489644615452483
1.00909329338628530.7897967712831379
0.82056082356963720.15379168745555616
2.99318573005473270.34543579552523984
0.83731494901795480.810119349113559
1.84989377086615560.11186312639075491
0.79864348884765690.5875124896588076
2.00412772168445260.6915070909179323
4.2240556446637120.3442663724650188
1.94990330497589270.8700132097492914
2.98745768026352380.808205815541499
3.0659273314892150.49160442382388414
2.88161578931266060.7328057998191583
2.85781001925149350.39938314903843464
0.77845165565383870.5354504488438098
3.24305523203814650.7393556116748278
4.0132703215497040.569011203662756
2.81451154671783940.6943160422603702
1.91259592597103010.35371234001492313
4.236040126338210.4155778038296022
4.2180903516420420.24639238863469104
3.8494767270981020.21866035522733784
1.98207116193151480.22728747265084115
2.13486343557437540.6530188065789416
0.91539620965199860.6849995836459672
2.81380110144793470.868808366582287
1.87275826021602220.697590587334583
4.2138464688042910.7869941993723283
3.24640620994204370.054774123323379276
1.1417158714713060.7546292452367565
3.8579210723123250.9936353001610227
3.93680750308554470.9702319861394001
4.14954123756714650.9548157677432825
1.97795669201679520.22546604456210773
2.0214435220706150.4254459101863406
1.97684906401709680.9111169553670773
3.1656118897456920.9445648966931448
0.98560014179000770.2520419991167283
1.8581503312089190.027371015824906864
3.09158798139164140.5501988481362968
4.0714249897892520.9738372307308305
1.1853228318768530.9591222449404715
4.0177444253893890.5671602112321875
2.907921226577160.48083979885980155
3.78660724312447350.8655859590804046
2.9441971701345140.0694571606662242
2.78281911351327740.6225106290225939
4.0697799300605080.9224603480171893
0.78424458078717060.18700487539883281
3.0773952805481220.21179779347166938
3.81500042649377360.4181464403606069
1.0763797882882740.6410620651668127
3.13310109181780.5856064192614485
2.1801914074107460.6745201531817407
3.20844538063262340.0567370392512806
2.89164597619780930.6753571923576499
2.036694315739890.6213908436925064
1.8759478581485820.22244967371095747
1.17689630024929310.2788303260944013
2.0901809630542290.23849788896293656
3.9534744556255310.3485998582193758
0.88265496789252830.4081267441800527
0.87879147613697080.5137956601251248
1.76583836985110180.14190452523901043
1.0455972671301860.22397456195988152
0.83719037841066110.996398361105594
2.94248134268714170.6580745507087566
4.0993740200823920.5637173765896986
1.08751627654943750.8001726293694157
2.8273041103292850.22387276972538217
2.99644469238850860.8589274960959142
1.18334706007171350.8323075439974053
3.880478699866740.4213947359498552
0.95174921781454340.8943592085408273
1.768249847565630.2266030921252029
2.0130091448939480.9741350476128602
3.9611883878472990.9921221717678421
1.78031137382426570.046836121816080545
2.08816596636458840.8212524740973319
3.17576282336098670.16362626503840627
2.2101402110154270.6902175471429484
1.2167735579748530.9390194666329318
4.0776549548980060.4909484824553295
2.13975706338253820.23655990729592613
2.09000575148672540.9231320499817033
3.1271542152184260.02212647541005819
1.98904534324881090.6070347210179184
3.8891551681173930.8054327605911276
2.82850618046347830.18267524521417788
3.78981971692565440.9942034287494077
1.16508759263563230.24794439300388094
1.89114917242930480.08784030377787089
4.0119238209425710.3827076222337582
3.7846729737133730.6401196266200835
4.1469300773784880.8594724440490045
1.80122419007952630.5743640780190793
0.79612028878587190.32550073923233824
3.7922842447558920.7818997197110137
2.1080403028134810.8281410033845884
3.06432819558836740.5045840112038369
4.0741711053431280.13916898846300385
1.96706391823923750.6528341367744794
0.88824937639516280.7146547486252668
3.76167195573254260.17834114894143083
3.19693955885152370.4459487663557117
0.78777852306703240.6882837111259372
3.22083221720861030.8023030112033331
1.05251996604918950.7821683036122776
0.78872244404170640.19568628922501163
2.96247686324456750.4358523793378436
2.04648741188338330.39198250169152393
3.11352777892138780.11360168459951026
2.10617558391210350.6642693805375591
0.91046736171434830.7557964922352066
2.7811987724154890.4644179392864445
2.03323890094838640.29075009103566896
0.76510891886003530.5565507355980254
2.9506657296631190.5872286665992463
3.0401844067709420.5760144499368227
4.16017618689966450.01563354657629501
1.8072683061510770.3312879052202624
4.2437632853301050.47239864678245225
3.9422873088232830.19619997571870074
2.90447547726741550.7483008772508714
3.88265311681619350.5023248535269951
2.0288605263728510.5264454911641667
2.9681270829736280.4107050281769109
3.8974711942350670.016168511270878594
4.0084261480817660.6891135870767084
0.90450094952487890.0028255728554108517
1.8972177196856280.821332885539736
3.0367212847756010.11271201175193979
1.94507484923431370.656538478901774
4.173043896589570.32526231681412365
2.2042488393354240.7992005896085951
4.2385214993286190.7647862761585806
1.15938391441513120.28313567425363695
3.0124899151026550.9837609431339588
0.94181147122043330.41226837667238525
2.7900503636512730.1738942370659159
3.2013887172819120.37372897688415463
3.07544044837156250.7911133365397495
3.7830837430045170.11713401014978575
1.12011311546150470.8916346864313749
2.0615076910923150.8244669978901201
3.19647910784920.4556905467926975
4.008710958077660.21992588087221654
3.19358102616396480.4762031536881922
3.2379747071246630.41012883792543287
1.24456439524632810.68072808956177
1.06279190041377820.9353666109683862
2.7844680204922230.9514084295478217
1.87643483856390430.880014210783719
2.83694780830631550.31322089348156024
2.0508421975189140.5769600659422053
0.82807355963134750.65590767426589
2.9623430351579350.06988563382132096
4.135029277997570.9470913943908873
2.78452415083122330.9193811550535141
3.89938963350588660.7318217460210769
2.90214901882118160.7594122645654858
2.03768799389502760.4704100989700123
1.06351530601658380.41601923074567215
0.98042140393878390.9764740463236247
3.0405154475217460.7713190608139815
2.7555314816374920.2989313514289098
3.103359404466510.3769511221635041
1.9291799198070140.7308833379516695
4.02749127253830250.12613981259427376
4.2156027473921070.6002177892737408
2.0215548193360940.6842244340377102
1.84040783350168930.23416319027706767
4.0780612824722620.668930559288584
0.89242683342386380.3970132824366758
2.96364594990007070.33380255641211753
2.01098082754497740.6182689195139303
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
-1.0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
-1
0
1
2
-1.00
-0.95
-0.90
-0.85
-0.80
-0.75
-0.70
-0.65
-0.60
-0.55
-0.50
-0.45
-0.40
-0.35
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1.05
1.10
1.15
1.20
1.25
1.30
1.35
1.40
1.45
1.50
1.55
1.60
1.65
1.70
1.75
1.80
1.85
1.90
1.95
2.00
y
using Gadfly, Random
set_default_plot_size(14cm, 8cm)
Random.seed!(1234)
plot(x=rand(10), y=rand(10), Stat.xticks(ticks=[0.0, 0.1, 0.9, 1.0]), Geom.point)
x
0.0
0.1
0.9
1.0
0.94645322623138340.0027764596470082337
0.131025656220859040.7285427707652774
0.9671427689153830.40573030892222206
0.83962193405807110.012846056572255682
0.63956159968027340.5239478383832243
0.5203549937237180.8406409194782338
0.0149088492850999450.5257956638691226
0.97213608245546870.08344008943212289
0.41129411794985050.5525344667850608
0.57986212013413240.749719144596962
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
-1.0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
-1
0
1
2
-1.00
-0.95
-0.90
-0.85
-0.80
-0.75
-0.70
-0.65
-0.60
-0.55
-0.50
-0.45
-0.40
-0.35
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1.05
1.10
1.15
1.20
1.25
1.30
1.35
1.40
1.45
1.50
1.55
1.60
1.65
1.70
1.75
1.80
1.85
1.90
1.95
2.00
y